A Robust Multi-Stereo Visual-Inertial Odometry Pipeline

Abstract

In this paper we present a novel multi-stereo visual-inertial odometry (VIO) framework which aims to improve the robustness of a robot's state estimate during aggressive motion and in visually challenging environments. Our system uses a fixed-lag smoother which jointly optimizes for poses and landmarks across all stereo pairs. We propose a 1-point RANdom SAmple Consensus (RANSAC) algorithm which is able to perform outlier rejection across features from all stereo pairs. To handle the problem of noisy extrinsics, we account for uncertainty in the calibration of each stereo pair and model it in both our front-end and back-end. The result is a VIO system which is able to maintain an accurate state estimate under conditions that have typically proven to be challenging for traditional state-of-the-art VIO systems. We demonstrate the benefits of our proposed multi-stereo algorithm by evaluating it with both simulated and real world data. We show that our proposed algorithm is able to maintain a state estimate in scenarios where traditional VIO algorithms fail.